A seller (respectively, a buyer in a procurement auction) has to make a number of decisions to conduct an auction. For example, the seller chooses a format from among a number of alternative formats: English, Dutch, Vickrey, sealed-bid first-price, etc. Then the seller decides the levels of a number of parameters: reserve price, bid increment, entry fees, lot size, etc. For any choice of the levels of these additional parameters, alternative formats may yield different outcomes from the seller's (buyer's) point of view. For example, whether or not English format is better than a sealed-bid first-price format depends on the specifics of the auction situation characterized typically by the bidders' attitudes towards risk, the distribution of bidders' private information and other relevant random elements.
A seller (buyer) conducting an auction to sell (procure) an item or items can improve the auction outcome in his/her favor by selecting an auction format based on the characteristics of the bidders.
As is known, the outcome of an auction (e.g., who gets what, who pays how much) is determined by bidding behavior of bidders. Bidding behavior depends on a number of factors including the auction rules. Different auction rules induce different behavior on the part of the bidders. A bidder's behavior under a given collection of auction rules in turn is determined by the bidder's private information. The structure of the private information held by the bidders is thus a key factor in evaluating alternative auction rules. This fundamental element of the auction environment is not directly observable and has to be estimated from available data.
Currently, the decisions on the auction format are left entirely to the person conducting the auction. There is little systematic data analysis to guide these decisions. Given the multiplicity of items bought/sold through auctions, it is typically too costly to hire expert analysts to configure the auction procedures for each case. Furthermore, fixed auction format is rarely optimal for every case to which it is applied. Sellers (buyers) typically must resort to decisions based on personal feelings and instinct.
Consider the decision to choose between first-price and English auction. Currently auction format choice decision is guided by rules of thumb, and is error prone. Certain characteristics of the market environment affect the revenues from English and first price auctions. In some environments English auction is better from a seller's point of view, in some a first price auction is better. However, in many markets such characteristics, and hence the appropriate auction format, are seldom fixed, and thus an auction format which is fixed once-and-for-all is often the wrong format.
Currently, there is not an integrated data collection, modeling, estimation and optimization solution for selecting the auction format optimally based on structural econometric analysis of available data. All decisions must be based on personal knowledge rather than a systematic analysis. As a result, a determination of an optimal auction format is often guesswork and may not provide optimal results.
Accordingly, there exists a need for an automated estimation and optimization solution for selecting the best auction format. A need exists for a method and/or system that provides automated decision support for selecting the best auction format based on structural analysis of data from related auctions. A need also exists for a method and/or system that accomplishes the above needs, and determines the latent elements of the auction environment taking into account the strategic and information conditions with minimal assumptions on the distributions of unobserved random elements.